To gain a deeper understanding of the above failures and biases, in Figure REF , we provide some image samples from the LAION-400M dataset [1]} with CLIP retrieval, using the same text descriptions as Figure REF .
CLIP retrieval is based on the CLIP embedding similarity between the web images and text descriptions, while the original text below each individual image is not used.
We observe an impressive consistency between CLIP retrieval and PromptGen: in both Figure REF  and Figure REF , most images have beard; in both Figure REF  and Figure REF , all images are female; in both Figure REF  and Figure REF , some images do not have exactly three windows. This consistency suggests that the failures and biases in Figure REF  should be mostly attributed to the CLIP model rather than to our PromptGen algorithm.
We believe that our observation sheds light on the intricacy of contrastive multi-modal (vision-language) pre-training, which is worthy of being further investigated.
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